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MODELING GROUND WATER FLOW USING FLUX BOUNDARY CONDITIONS1

 

作者: Rolando Bravo,   Jerry R. Rogers,   Theodore G. Cleveland,  

 

期刊: JAWRA Journal of the American Water Resources Association  (WILEY Available online 1996)
卷期: Volume 32, issue 1  

页码: 39-46

 

ISSN:1093-474X

 

年代: 1996

 

DOI:10.1111/j.1752-1688.1996.tb03432.x

 

出版商: Blackwell Publishing Ltd

 

关键词: ground water;prescribed head;flux boundary;inverse estimation;regional model

 

数据来源: WILEY

 

摘要:

ABSTRACT:Determination of the boundary conditions for modeling ground water flow is a critical point especially in regional models. Normally the regional models require model areas that are greater than the given area of interest. This work focuses on the prediction of hydraulic heads in regional models using flux boundary conditions. The model uses flux boundary conditions that were estimated using a radial flow analog and Darcy's law. The regional model that is presented uses no parameter identification (inverse estimation) procedures. In the present work, the Houston area was used. The simulation of the hydrological conditions of the Chicot and Evangeline Aquifers that underlie the Houston area were made using the available information about the geological profile in the Houston region and the current information about the existing production wells. The regional model works as a forward problem. The system parameters such as hydraulic conductivity, specific storage, and hydrological stresses were specified, and the model predicts the hydraulic head. Actual data from piezometers operated by the U.S. Geological Survey (USGS) in many places throughout Houston were used as initial conditions. Some piezometric head data were generated using the regional variable theory called kriging to supply head estimates in areas where data were unavailable. The Modular Three Dimensional Finite Difference Groundwater Flow Model developed by the USGS was used to predict the hydraulic heads. The predicted ground water heads are compared to the actual data. The results show that the model performs well for locations where data were available.

 

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